Upload dependency detector checkpoint

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-classification
+base_model: google-bert/bert-base-uncased
+datasets:
+- hotpotqa/hotpot_qa
+tags:
+- prompt-compression
+- dependency-detection
+- referential-dangling
+- research
+---
+
+# Referential Dangling Dependency Detector
+
+This is the sentence-pair dependency detector released with **Relevant but
+Incomplete: Referential Dangling as a Paradigm-Level Failure Mode in Hard
+Prompt Compression**.
+
+The model scores whether a candidate sentence supplies a necessary dependency
+for a retained sentence, conditioned on the question. It is used by the
+repository's automatic context-restoration experiments.
+
+## Model details
+
+- **Architecture:** BERT sequence classifier with two labels
+- **Base model:** `google-bert/bert-base-uncased`
+- **Labels:** `NOT_DEPENDENCY` (0), `DEPENDENCY` (1)
+- **Maximum training input length:** 256 tokens
+- **Input format:** `retained sentence [SEP] candidate support [SEP] question`
+
+## Training data
+
+Training pairs were constructed from the HotpotQA training split. Positive
+pairs contain a retained sentence and a missing gold-support sentence that
+share a discriminative entity. Negatives include entity-overlapping hard
+negatives and unrelated deleted sentences. Splitting is grouped by source
+example to prevent sentence pairs from the same example appearing in both the
+training and validation partitions.
+
+See `src/build_train_tight.py` and `src/train_detector.py` in the
+[Referential-Dangling repository](https://github.com/JusperLee/Referential-Dangling)
+for the data construction and training code.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_id = "JusperLee/referential-dangling-detector"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id).eval()
+
+retained = "The film was directed by Jane Smith."
+candidate = "Jane Smith is a Canadian filmmaker."
+question = "What nationality is the film's director?"
+text = f"{retained} [SEP] {candidate} [SEP] {question}"
+inputs = tokenizer(text, truncation=True, max_length=256, return_tensors="pt")
+
+with torch.no_grad():
+    probability = model(**inputs).logits.softmax(dim=-1)[0, 1].item()
+
+print(probability)
+```
+
+For the paper's restoration pipeline, use `BertDependencyDetector` from
+`src/beaver2_bert.py`.
+
+## Intended use and limitations
+
+This checkpoint is intended for research on dependency loss and automatic
+support restoration in compressed English QA contexts. It is not a general
+factuality, entailment, or coreference model. Its predictions depend on the
+candidate-generation procedure and may not transfer reliably to other domains,
+languages, or substantially different compression settings without evaluation.
+
+## Citation
+
+```bibtex
+@misc{referentialdangling,
+  title={Relevant but Incomplete: Referential Dangling as a Paradigm-Level Failure Mode in Hard Prompt Compression},
+  note={Research code and model release}
+}
+```

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "NOT_DEPENDENCY",
+    "1": "DEPENDENCY"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "DEPENDENCY": 1,
+    "NOT_DEPENDENCY": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.10.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:975c25527e583c602b92e063deca8f5c1b0e04bee4843bd8bdbb30b76183560f
+size 437958624

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}